mirror of https://github.com/InternLM/InternLM
213 lines
7.7 KiB
Python
213 lines
7.7 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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# adopted from https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/engine
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from typing import Any, Callable, Iterable, List, Optional
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import torch
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from internlm.core.context import global_context as gpc
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from internlm.core.engine import Engine
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from internlm.utils.common import conditional_context
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from internlm.utils.logger import get_logger
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from internlm.utils.timeout import llm_timeout
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from .base_scheduler import BaseScheduler, SchedulerHook
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logger = get_logger(__file__)
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class NonPipelineScheduler(BaseScheduler):
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"""A helper schedule class for no pipeline parallelism running environment.
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During one process, it loads a batch of dataset and feeds it to the model.
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After getting the output and calculating the loss, it will use :meth:`step`
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to update the parameters if it is in training mode.
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Args:
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data_process_func (Callable, optional): The preprocessing function which receives a batch of data
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and returns a tuple in the form of (data, label), and it will be executed in load_batch.
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gradient_accumulation_steps(int, optional): the steps of gradient accumulation, 1 for disable
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gradient accumulation.
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Examples:
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>>> # this shows an example of customized data_process_func
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>>> def data_process_func(dataloader_output):
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>>> item1, item2, item3 = dataloader_output
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>>> data = (item1, item2)
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>>> label = item3
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>>> return data, label
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"""
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def __init__(
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self,
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data_process_func: Callable = None,
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gradient_accumulation_size: int = 1,
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scheduler_hooks: Optional[List[SchedulerHook]] = None,
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):
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self._grad_accum_size = gradient_accumulation_size
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self._grad_accum_offset = 0
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self._hooks = scheduler_hooks
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super().__init__(data_process_func)
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def pre_processing(self, engine: Engine):
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"""Performs actions before running the schedule.
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Args:
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engine (internlm.core.Engine): InternLM engine for training and inference.
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"""
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pass
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def _call_hooks(self, func_name: str, *args, **kwargs) -> None:
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for hook in self._hooks:
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getattr(hook, func_name)(self, *args, **kwargs)
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def _load_accum_batch(self, data: Any, label: Any):
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"""Loads a batch of data and label for gradient accumulation.
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Args:
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data (Any): The data to be loaded.
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label (Any): The label to be loaded.
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"""
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_data, _label = self._load_micro_batch(data=data, label=label, offset=self._grad_accum_offset)
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self._grad_accum_offset += 1
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if self.data_process_func:
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_data["input_ids"] = self.data_process_func(_data["input_ids"], _data["cu_seqlens"])
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_label = self.data_process_func(_label, _data["cu_seqlens"])
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_data.pop("cu_seqlens")
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_data.pop("indexes")
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return _data, _label
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def _train_one_batch(
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self,
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data: Any,
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label: Any,
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engine: Engine,
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forward_only: bool = False,
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return_loss: bool = True,
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scale_loss: int = 1,
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):
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"""Trains one batch of data.
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Args:
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data (Any): The data to be trained.
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label (Any): The label for the data.
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engine (internlm.core.Engine): InternLM engine for training and inference.
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forward_only (bool, optional): If True, the model is run for the forward pass, else back propagation will
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be executed.
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return_loss (bool, optional): Loss will be returned if True.
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scale_loss (int, optional): The scale factor for the loss.
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"""
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# forward
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with conditional_context(torch.no_grad(), enable=forward_only):
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self._call_hooks("before_forward", data)
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if hasattr(gpc.config.model, "num_experts"):
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# moe is used
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output, moe_losses = self._call_engine(engine, data)
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else:
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output = self._call_engine(engine, data)
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self._call_hooks("after_forward", output)
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self._call_hooks("post_helper_func", output, label)
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if return_loss:
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self._call_hooks("before_criterion", output, label)
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loss = self._call_engine_criterion(engine, output, label)
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self._call_hooks("after_criterion", loss)
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moe_loss = (
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sum(moe_losses) * gpc.config.loss.moe_loss_coeff
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if hasattr(gpc.config.model, "num_experts")
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else torch.tensor(0.0, device=torch.cuda.current_device(), dtype=gpc.config.model.get("dtype"))
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)
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moe_loss /= scale_loss
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loss /= scale_loss
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loss += moe_loss
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# backward
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if not forward_only:
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self._call_hooks("before_backward", None, None)
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engine.backward(loss)
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self._call_hooks("after_backward", None)
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if not return_loss:
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loss, moe_loss = None, None
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return output, loss, moe_loss
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@llm_timeout(func_name="nopp_forward_backward_step")
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def forward_backward_step(
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self,
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engine: Engine,
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data_iter: Iterable,
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forward_only: bool = False,
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return_loss: bool = True,
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return_output_label: bool = True,
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):
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"""The process function that loads a batch of dataset and feeds it to the model.
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The returned labels and loss will None if :attr:`return_loss` is False.
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Args:
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engine (internlm.core.Engine): InternLM engine for training and inference.
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data_iter (Iterable): Dataloader as the form of an iterator, obtained by calling iter(dataloader).
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forward_only (bool, optional):
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If True, the model is run for the forward pass, else back propagation will be executed.
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return_loss (bool, optional): Loss will be returned if True.
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return_output_label (bool, optional): Output and label will be returned if True.
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Returns:
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Tuple[:class:`torch.Tensor`]: A tuple of (output, label, loss), loss and label could be None.
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"""
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assert (
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forward_only or return_loss
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), "The argument 'return_loss' has to be True when 'forward_only' is False, but got False."
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batch_data, actual_batch_size = engine.load_batch(data_iter)
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self._grad_accum_size = actual_batch_size # Rampup or variable bsz size.
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data, label = batch_data
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loss = 0 if return_loss else None
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moe_loss = 0 if return_loss else None
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outputs = []
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labels = []
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# reset accumulation microbatch offset
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self._grad_accum_offset = 0
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for _current_accum_step in range(self._grad_accum_size):
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if engine.optimizer is not None:
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if _current_accum_step == self._grad_accum_size - 1:
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engine.optimizer.skip_grad_reduce = False
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else:
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engine.optimizer.skip_grad_reduce = True
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_data, _label = self._load_accum_batch(data, label)
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_output, _loss, _moe_loss = self._train_one_batch(
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_data, _label, engine, forward_only, return_loss, self._grad_accum_size
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)
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if return_loss:
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loss += _loss
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moe_loss += _moe_loss
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if return_output_label:
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outputs.append(_output)
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labels.append(_label)
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if not return_output_label:
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outputs, labels = None, None
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# Compatible for non-moe
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if hasattr(gpc.config.model, "num_experts"):
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return outputs, labels, loss, moe_loss
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else:
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return outputs, labels, loss
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